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by Frank Emmert-Streib,Matthias Dehmer

  • ISBN: 3527318224
  • Category: Other
  • Author: Frank Emmert-Streib,Matthias Dehmer
  • Subcategory: Medicine & Health Sciences
  • Other formats: lrf txt mbr docx
  • Language: English
  • Publisher: Wiley-Blackwell; 1 edition (March 17, 2008)
  • Pages: 438 pages
  • FB2 size: 1393 kb
  • EPUB size: 1155 kb
  • Rating: 4.4
  • Votes: 924
Download Analysis of Microarray Data: A Network-Based Approach fb2

Frank Emmert-Streib, Matthias Dehmer. This book is the first to focus on the application of mathematical networks for analyzing microarray data.

Frank Emmert-Streib, Matthias Dehmer. This method goes well beyond the standard clustering methods traditionally used. From the contents: Understanding and Preprocessing Microarray Data. Clustering of Microarray Data. Bilayer Verification Algorithm. Probabilistic Boolean Networks as Models for Gene Regulation. Estimating Transcriptional Regulatory Networks by a Bayesian Network.

Analysis of Microarray Data is the first to focus on the application of mathematical networks for analyzing .

Analysis of Microarray Data is the first to focus on the application of mathematical networks for analyzing microarray data which goes well beyond the standard clustering methods traditionally used. Analysis of Microarray Data includes: Understanding and Preprocessing Microarray Data.

Frank Emmert-Streib, Matthias Dehmer

Frank Emmert-Streib, Matthias Dehmer. Introduction to DNA Microarrays Comparative Analysis of Clustering Methods for Microarray Data Finding Verified Edges in Genetic/Gene Networks: Bilayer Verification for Network Recovery in the Presence Computational Inference of Biological Causal Networks - Analysis of Therapeutic Compound Effects Reverse Engineering Gene Regulatory Networks with Various Machine Learning Methods Statistical Methods for Inference of Genetic Networks and Regulatory Modules A Model of Genetic Networks.

Analysis of Microarray Data: A Network-Based Approach (Hardback). Hardback 438 Pages, Published: 13/02/2008.

Analysis of microarray data: a network-based approach. M Dehmer, F Emmert-Streib. M Dehmer, K Varmuza, S Borgert, F Emmert-Streib. Journal of chemical information and modeling 49 (7), 1655-1663, 2009. Analysis of Complex Networks

Analysis of microarray data: a network-based approach. John Wiley & Sons, 2008. Entropy and the complexity of graphs revisited. A Mowshowitz, M Dehmer. Entropy 14 (3), 559-570, 2012. Analysis of Complex Networks. F Emmert-Streib, M Dehmer. Vch Verlagsgesellschaft Mbh, 2008. Statistical modelling of molecular descriptors in QSAR/QSPR. K Varmuza, M Dehmer, D Bonchev. Wiley-Blackwell, Weinheim, 2012. Bagging statistical network inference from large-scale gene expression data. R de Matos Simoes, F Emmert-Streib. PloS one 7 (3), e33624, 2012. The chronic fatigue syndrome: a comparative pathway analysis. A Musa, LS Ghoraie, SD Zhang, G Glazko, O Yli-Harja, M Dehmer,. Briefings in bioinformatics 19 (3), 506-523, 2017.

This book is the first to focus on the application of mathematical networks for analyzing microarray data. Frank Emmert-Streib studied physics at the University of Siegen, Germany, and received his PhD in Theoretical Physics from the University of Bremen, Germany. He is currently Senior Fellow at the University of Washington in Seattle, USA, in Biostatistics and Genome Sciences. Matthias Dehmer studied mathematics at the University of Siegen, Germany, and received his PhD in Computer Science from the Technical University of Darmstadt, Germany.

This item: Analysis of Microarray Data: A Network-Based Approach. Frank Emmert-Streib studied physics at the University of Siegen, Germany, and received his PhD in Theoretical Physics from the University of Bremen, Germany

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This book is the first to focus on the application of mathematical networks for analyzing microarray data. This method goes well beyond the standard clustering methods traditionally used. From the contents:* Understanding and Preprocessing Microarray Data* Clustering of Microarray Data* Reconstruction of the Yeast Cell Cycle by Partial Correlations of Higher Order* Bilayer Verification Algorithm* Probabilistic Boolean Networks as Models for Gene Regulation* Estimating Transcriptional Regulatory Networks by a Bayesian Network* Analysis of Therapeutic Compound Effects* Statistical Methods for Inference of Genetic Networks and Regulatory Modules* Identification of Genetic Networks by Structural Equations* Predicting Functional Modules Using Microarray and Protein Interaction Data* Integrating Results from Literature Mining and Microarray Experiments to Infer Gene NetworksThe book is for both, scientists using the technique as well as those developing new analysis techniques.

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